Antibodies are a key component of the adaptive immune system, released in response to disease in order to target foreign molecular surfaces. Due to their capacity for high target affinity and specificity, they?ve become one of the fastest growing classes of therapeutic molecules addressing a range of disease including infectious diseases, auto-immune diseases, and cancer. Camelids, including llamas, camels, and alpacas, produce a unique repertoire of antibodies that includes both dual chain antibodies and single chain antibodies. Single chain antibodies, due to their comparative structural simplicity, are simpler to express and develop at the scale required for therapeutics. In addition, single chain antibodies are able to bind to small epitopes, such as enzyme active sites, that would be hidden to larger, dual chain antibodies. The binding domain of the single chain antibody may be small enough to infiltrate traditionally difficult to access tissues, including crossing the blood-brain barrier. Current approaches to single chain antibody discovery require the collection of cells that encode the antibody genes, including memory B cells and plasma cells. Target-specific antibodies are selected after the antibody transcripts are cloned into a display system, such as phage or yeast. While memory B cells and plasma cells represent only a minute fraction of the cells located in peripheral blood, target- specific antibodies are present in high concentration in blood after an infection. Each plasma cell can secrete thousands of antibodies per minute. Digital Proteomics is developing Alicanto, a technology that utilizes the antibodies circulating in blood to identify target-specific antibodies. Alicanto integrates two sources of information about the antibody repertoire. First, Alicanto constructs a database of potential antigen-specific antibodies by performing next-generation sequencing of antibody transcripts. Next, Alicanto enriches for target-specific antibodies from the blood using affinity chromatography and subjects the antibodies to tandem mass spectrometry. Finally, Alicanto uses machine learning models to integrate the sequencing and mass spectrometry data to derive a collection of target-specific antibody candidates. For single chain antibody discovery, Alicanto will use specialized primers and enrichment techniques to isolate only the subset of the antibody repertoire that contains the single chain antibodies. Alicanto will be used to discover high affinity, single chain antibodies for development as therapeutic molecules.